R vs Python: Different similarities and similar differences
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A debate about which language is better suited for Datascience, R or Python, can set off diehard fans of these languages into a tizzy. This post tries to look at some of the different similarities and similar differences between these languages. To a large extent the ease or difficulty in learning R or Python is subjective. I have heard that R has a steeper learning curve than Python and also vice versa. This probably depends on the degree of familiarity with the languuge To a large extent both R an Python do the same thing in just slightly different ways and syntaxes. The ease or the difficulty in the R/Python construct’s largely is in the ‘eyes of the beholder’ nay, programmer’ we could say. I include my own experience with the languages below.
1. R data types
R has the following data types
- Character
- Integer
- Double (floating point numeric)
- Logical
- Complex
- Raw
Python has several data types
- Int
- float
- Long
- Complex and so on
2. R Vector vs Python List
A common data type in R is the vector. Python has a similar data type, the list
# R vectors a<-c(4,5,1,3,4,5) print(a[3]) ## [1] 1 print(a[3:4]) # R does not always need the explicit print. ## [1] 1 3 #R type of variable print(class(a)) ## [1] "numeric" # Length of a print(length(a)) ## [1] 6 # Python lists a=[4,5,1,3,4,5] # print(a[2]) # Some python IDEs require the explicit print print(a[2:5]) print(type(a)) # Length of a print(len(a)) ## 1 ## [1, 3, 4] ## <class 'list'> ## 6
2a. Other data types – Python
Python also has certain other data types like the tuple, dictionary etc as shown below. R does not have as many of the data types, nevertheless we can do everything that Python does in R
# Python tuple
b = (4,5,7,8)
print(b)
#Python dictionary
c={'name':'Ganesh','age':54,'Work':'Professional'}
print(c)
#Print type of variable c
## (4, 5, 7, 8)
## {'name': 'Ganesh', 'age': 54, 'Work': 'Professional'}
2.Type of Variable
To know the type of the variable in R we use ‘class’, In Python the corresponding command is ‘type’
#R - Type of variable a<-c(4,5,1,3,4,5) print(class(a)) ## [1] "numeric" #Python - Print type of tuple a a=[4,5,1,3,4,5] print(type(a)) b=(4,3,"the",2) print(type(b)) ## <class 'list'> ## <class 'tuple'>
3. Length
To know length in R, use length()
#R - Length of vector # Length of a a<-c(4,5,1,3,4,5) print(length(a)) ## [1] 6
To know the length of a list,tuple or dict we can use len()
# Python - Length of list , tuple etc # Length of a a=[4,5,1,3,4,5] print(len(a)) # Length of b b = (4,5,7,8) print(len(b)) ## 6 ## 4
4. Accessing help
To access help in R we use the ‘?’ or the ‘help’ function
#R - Help - To be done in R console or RStudio #?sapply #help(sapply)
Help in python on any topic involves
#Python help - This can be done on a (I)Python console #help(len) #?len
5. Subsetting
The key difference between R and Python with regards to subsetting is that in R the index starts at 1. In Python it starts at 0, much like C,C++ or Java To subset a vector in R we use
#R - Subset a<-c(4,5,1,3,4,8,12,18,1) print(a[3]) ## [1] 1 # To print a range or a slice. Print from the 3rd to the 5th element print(a[3:6]) ## [1] 1 3 4 8
Python also uses indices. The difference in Python is that the index starts from 0/
#Python - Subset a=[4,5,1,3,4,8,12,18,1] # Print the 4th element (starts from 0) print(a[3]) # Print a slice from 4 to 6th element print(a[3:6]) ## 3 ## [3, 4, 8]
6. Operations on vectors in R and operation on lists in Python
In R we can do many operations on vectors for e.g. element by element addition, subtraction, exponentation,product etc. as show
#R - Operations on vectors a<- c(5,2,3,1,7) b<- c(1,5,4,6,8) #Element wise Addition print(a+b) ## [1] 6 7 7 7 15 #Element wise subtraction print(a-b) ## [1] 4 -3 -1 -5 -1 #Element wise product print(a*b) ## [1] 5 10 12 6 56 # Exponentiating the elements of a vector print(a^2) ## [1] 25 4 9 1 49
In Python to do this on lists we need to use the ‘map’ and the ‘lambda’ function as follows
# Python - Operations on list a =[5,2,3,1,7] b =[1,5,4,6,8] #Element wise addition with map & lambda print(list(map(lambda x,y: x+y,a,b))) #Element wise subtraction print(list(map(lambda x,y: x-y,a,b))) #Element wise product print(list(map(lambda x,y: x*y,a,b))) # Exponentiating the elements of a list print(list(map(lambda x: x**2,a))) ## [6, 7, 7, 7, 15] ## [4, -3, -1, -5, -1] ## [5, 10, 12, 6, 56] ## [25, 4, 9, 1, 49]
However if we create ndarrays from lists then we can do the element wise addition,subtraction,product, etc. like R. Numpy is really a powerful module with many, many functions for matrix manipulations
import numpy as np a =[5,2,3,1,7] b =[1,5,4,6,8] a=np.array(a) b=np.array(b) #Element wise addition print(a+b) #Element wise subtraction print(a-b) #Element wise product print(a*b) # Exponentiating the elements of a list print(a**2) ## [ 6 7 7 7 15] ## [ 4 -3 -1 -5 -1] ## [ 5 10 12 6 56] ## [25 4 9 1 49]
7. Getting the index of element
To determine the index of an element which satisifies a specific logical condition in R use ‘which’. In the code below the index of element which is equal to 1 is 4
# R - Which a<- c(5,2,3,1,7) print(which(a == 1)) ## [1] 4
In Python array we can use np.where to get the same effect. The index will be 3 as the index starts from 0
# Python - np.where import numpy as np a =[5,2,3,1,7] a=np.array(a) print(np.where(a==1)) ## (array([3], dtype=int64),)
8. Data frames
R, by default comes with a set of in-built datasets. There are some datasets which come with the SkiKit- Learn package
# R # To check built datasets use #data() - In R console or in R Studio #iris - Don't print to console
We can use the in-built data sets that come with Scikit package
#Python import sklearn as sklearn import pandas as pd from sklearn import datasets # This creates a Sklearn bunch data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names)
9. Working with dataframes
With R you can work with dataframes directly. For more complex dataframe operations in R there are convenient packages like dplyr, reshape2 etc. For Python we need to use the Pandas package. Pandas is quite comprehensive in the list of things we can do with data frames The most common operations on a dataframe are
- Check the size of the dataframe
- Take a look at the top 5 or bottom 5 rows of dataframe
- Check the content of the dataframe
a.Size
In R use dim()
#R - Size dim(iris) ## [1] 150 5
For Python use .shape
#Python - size import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) iris.shape
b. Top & bottom 5 rows of dataframe
To know the top and bottom rows of a data frame we use head() & tail as shown below for R and Python
#R head(iris,5) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa tail(iris,5) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 146 6.7 3.0 5.2 2.3 virginica ## 147 6.3 2.5 5.0 1.9 virginica ## 148 6.5 3.0 5.2 2.0 virginica ## 149 6.2 3.4 5.4 2.3 virginica ## 150 5.9 3.0 5.1 1.8 virginica #Python import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) print(iris.head(5)) print(iris.tail(5)) ## sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) ## 0 5.1 3.5 1.4 0.2 ## 1 4.9 3.0 1.4 0.2 ## 2 4.7 3.2 1.3 0.2 ## 3 4.6 3.1 1.5 0.2 ## 4 5.0 3.6 1.4 0.2 ## sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) ## 145 6.7 3.0 5.2 2.3 ## 146 6.3 2.5 5.0 1.9 ## 147 6.5 3.0 5.2 2.0 ## 148 6.2 3.4 5.4 2.3 ## 149 5.9 3.0 5.1 1.8
c. Check the content of the dataframe
#R summary(iris) ## Sepal.Length Sepal.Width Petal.Length Petal.Width ## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 ## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 ## Median :5.800 Median :3.000 Median :4.350 Median :1.300 ## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 ## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 ## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 ## Species ## setosa :50 ## versicolor:50 ## virginica :50 ## ## ## str(iris) ## 'data.frame': 150 obs. of 5 variables: ## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... ## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... ## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... ## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... ## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... #Python import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) print(iris.info()) ## <class 'pandas.core.frame.DataFrame'> ## RangeIndex: 150 entries, 0 to 149 ## Data columns (total 4 columns): ## sepal length (cm) 150 non-null float64 ## sepal width (cm) 150 non-null float64 ## petal length (cm) 150 non-null float64 ## petal width (cm) 150 non-null float64 ## dtypes: float64(4) ## memory usage: 4.8 KB ## None
d. Check column names
#R names(iris) ## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" ## [5] "Species" colnames(iris) ## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" ## [5] "Species" #Python import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) #Get column names print(iris.columns) ## Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', ## 'petal width (cm)'], ## dtype='object')
e. Rename columns
In R we can assign a vector to column names
#R
colnames(iris) <- c("lengthOfSepal","widthOfSepal","lengthOfPetal","widthOfPetal","Species")
colnames(iris)
## [1] "lengthOfSepal" "widthOfSepal" "lengthOfPetal" "widthOfPetal"
## [5] "Species"
In Python we can assign a list to s.columns
#Python import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) iris.columns = ["lengthOfSepal","widthOfSepal","lengthOfPetal","widthOfPetal"] print(iris.columns) ## Index(['lengthOfSepal', 'widthOfSepal', 'lengthOfPetal', 'widthOfPetal'], dtype='object')
f.Details of dataframe
#Python import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) print(iris.info()) ## <class 'pandas.core.frame.DataFrame'> ## RangeIndex: 150 entries, 0 to 149 ## Data columns (total 4 columns): ## sepal length (cm) 150 non-null float64 ## sepal width (cm) 150 non-null float64 ## petal length (cm) 150 non-null float64 ## petal width (cm) 150 non-null float64 ## dtypes: float64(4) ## memory usage: 4.8 KB ## None
g. Subsetting dataframes
# R #To subset a dataframe 'df' in R we use df[row,column] or df[row vector,column vector] #df[row,column] iris[3,4] ## [1] 0.2 #df[row vector, column vector] iris[2:5,1:3] ## lengthOfSepal widthOfSepal lengthOfPetal ## 2 4.9 3.0 1.4 ## 3 4.7 3.2 1.3 ## 4 4.6 3.1 1.5 ## 5 5.0 3.6 1.4 #If we omit the row vector, then it implies all rows or if we omit the column vector # then implies all columns for that row iris[2:5,] ## lengthOfSepal widthOfSepal lengthOfPetal widthOfPetal Species ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa # In R we can all specific columns by column names iris$Sepal.Length[2:5] ## NULL #Python # To select an entire row we use .iloc. The index can be used with the ':'. If # .iloc[start row: end row]. If start row is omitted then it implies the beginning of # data frame, if end row is omitted then it implies all rows till end #Python import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) print(iris.iloc[3]) print(iris[:5]) # In python we can select columns by column name as follows print(iris['sepal length (cm)'][2:6]) #If you want to select more than 2 columns then you must use the double '[[]]' since the # index is a list itself print(iris[['sepal length (cm)','sepal width (cm)']][4:7]) ## sepal length (cm) 4.6 ## sepal width (cm) 3.1 ## petal length (cm) 1.5 ## petal width (cm) 0.2 ## Name: 3, dtype: float64 ## sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) ## 0 5.1 3.5 1.4 0.2 ## 1 4.9 3.0 1.4 0.2 ## 2 4.7 3.2 1.3 0.2 ## 3 4.6 3.1 1.5 0.2 ## 4 5.0 3.6 1.4 0.2 ## 2 4.7 ## 3 4.6 ## 4 5.0 ## 5 5.4 ## Name: sepal length (cm), dtype: float64 ## sepal length (cm) sepal width (cm) ## 4 5.0 3.6 ## 5 5.4 3.9 ## 6 4.6 3.4
h. Computing Mean, Standard deviation
#R #Mean mean(iris$lengthOfSepal) ## [1] 5.843333 #Standard deviation sd(iris$widthOfSepal) ## [1] 0.4358663 #Python #Mean import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) # Convert to Pandas dataframe print(iris['sepal length (cm)'].mean()) #Standard deviation print(iris['sepal width (cm)'].std()) ## 5.843333333333335 ## 0.4335943113621737
i. Boxplot
Boxplot can be produced in R using baseplot
#R boxplot(iris$lengthOfSepal)
Matplotlib is a popular package in Python for plots
#Python import sklearn as sklearn import pandas as pd import matplotlib.pyplot as plt from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) img=plt.boxplot(iris['sepal length (cm)']) plt.show(img)
j.Scatter plot
#R plot(iris$widthOfSepal,iris$lengthOfSepal)
#Python import matplotlib.pyplot as plt import sklearn as sklearn import pandas as pd from sklearn import datasets data = datasets.load_iris() # Convert to Pandas dataframe iris = pd.DataFrame(data.data, columns=data.feature_names) img=plt.scatter(iris['sepal width (cm)'],iris['sepal length (cm)']) #plt.show(img)
k. Read from csv file
#R
tendulkar= read.csv("tendulkar.csv",stringsAsFactors = FALSE,na.strings=c(NA,"-"))
#Dimensions of dataframe
dim(tendulkar)
## [1] 347 13
names(tendulkar)
## [1] "X" "Runs" "Mins" "BF" "X4s"
## [6] "X6s" "SR" "Pos" "Dismissal" "Inns"
## [11] "Opposition" "Ground" "Start.Date"
Use pandas.read_csv() for Python
#Python
import pandas as pd
#Read csv
tendulkar= pd.read_csv("tendulkar.csv",na_values=["-"])
print(tendulkar.shape)
print(tendulkar.columns)
## (347, 13)
## Index(['Unnamed: 0', 'Runs', 'Mins', 'BF', '4s', '6s', 'SR', 'Pos',
## 'Dismissal', 'Inns', 'Opposition', 'Ground', 'Start Date'],
## dtype='object')
l. Clean the dataframe in R and Python.
The following steps are done for R and Python
1.Remove rows with ‘DNB’
2.Remove rows with ‘TDNB’
3.Remove rows with absent
4.Remove the “*” indicating not out
5.Remove incomplete rows with NA for R or NaN in Python
6.Do a scatter plot
#R
# Remove rows with 'DNB'
a <- tendulkar$Runs != "DNB"
tendulkar <- tendulkar[a,]
dim(tendulkar)
## [1] 330 13
# Remove rows with 'TDNB'
b <- tendulkar$Runs != "TDNB"
tendulkar <- tendulkar[b,]
# Remove rows with absent
c <- tendulkar$Runs != "absent"
tendulkar <- tendulkar[c,]
dim(tendulkar)
## [1] 329 13
# Remove the "* indicating not out
tendulkar$Runs <- as.numeric(gsub("\\*","",tendulkar$Runs))
dim(tendulkar)
## [1] 329 13
# Select only complete rows - complete.cases()
c <- complete.cases(tendulkar)
#Subset the rows which are complete
tendulkar <- tendulkar[c,]
dim(tendulkar)
## [1] 327 13
# Do some base plotting - Scatter plot
plot(tendulkar$BF,tendulkar$Runs)
#Python
import pandas as pd
import matplotlib.pyplot as plt
#Read csv
tendulkar= pd.read_csv("tendulkar.csv",na_values=["-"])
print(tendulkar.shape)
# Remove rows with 'DNB'
a=tendulkar.Runs !="DNB"
tendulkar=tendulkar[a]
print(tendulkar.shape)
# Remove rows with 'TDNB'
b=tendulkar.Runs !="TDNB"
tendulkar=tendulkar[b]
print(tendulkar.shape)
# Remove rows with absent
c= tendulkar.Runs != "absent"
tendulkar=tendulkar[c]
print(tendulkar.shape)
# Remove the "* indicating not out
tendulkar.Runs= tendulkar.Runs.str.replace(r"[*]","")
#Select only complete rows - dropna()
tendulkar=tendulkar.dropna()
print(tendulkar.shape)
tendulkar.Runs = tendulkar.Runs.astype(int)
tendulkar.BF = tendulkar.BF.astype(int)
#Scatter plot
plt.scatter(tendulkar.BF,tendulkar.Runs)
## (347, 13)
## (330, 13)
## (329, 13)
## (329, 13)
## (327, 13)
m.Chaining operations on dataframes
To chain a set of operations we need to use an R package like dplyr. Pandas does this The following operations are done on tendulkar data frame by dplyr for R and Pandas for Python below
- Group by ground
- Compute average runs in each ground
- Arrange in descending order
#R
library(dplyr)
tendulkar1 <- tendulkar %>% group_by(Ground) %>% summarise(meanRuns= mean(Runs)) %>%
arrange(desc(meanRuns))
head(tendulkar1,10)
## # A tibble: 10 × 2
## Ground meanRuns
## <chr> <dbl>
## 1 Multan 194.00000
## 2 Leeds 193.00000
## 3 Colombo (RPS) 143.00000
## 4 Lucknow 142.00000
## 5 Dhaka 132.75000
## 6 Manchester 93.50000
## 7 Sydney 87.22222
## 8 Bloemfontein 85.00000
## 9 Georgetown 81.00000
## 10 Colombo (SSC) 77.55556
#Python
import pandas as pd
#Read csv
tendulkar= pd.read_csv("tendulkar.csv",na_values=["-"])
print(tendulkar.shape)
# Remove rows with 'DNB'
a=tendulkar.Runs !="DNB"
tendulkar=tendulkar[a]
# Remove rows with 'TDNB'
b=tendulkar.Runs !="TDNB"
tendulkar=tendulkar[b]
# Remove rows with absent
c= tendulkar.Runs != "absent"
tendulkar=tendulkar[c]
# Remove the "* indicating not out
tendulkar.Runs= tendulkar.Runs.str.replace(r"[*]","")
#Select only complete rows - dropna()
tendulkar=tendulkar.dropna()
tendulkar.Runs = tendulkar.Runs.astype(int)
tendulkar.BF = tendulkar.BF.astype(int)
tendulkar1= tendulkar.groupby('Ground').mean()['Runs'].sort_values(ascending=False)
print(tendulkar1.head(10))
## (347, 13)
## Ground
## Multan 194.000000
## Leeds 193.000000
## Colombo (RPS) 143.000000
## Lucknow 142.000000
## Dhaka 132.750000
## Manchester 93.500000
## Sydney 87.222222
## Bloemfontein 85.000000
## Georgetown 81.000000
## Colombo (SSC) 77.555556
## Name: Runs, dtype: float64
9. Functions
product <- function(a,b){
c<- a*b
c
}
product(5,7)
## [1] 35
def product(a,b):
c = a*b
return c
print(product(5,7))
## 35
Conclusion
Personally, I took to R, much like a ‘duck takes to water’. I found the R syntax very simple and mostly intuitive. R packages like dplyr, ggplot2, reshape2, make the language quite irrestible. R is weakly typed and has only numeric and character types as opposed to the full fledged data types in Python.
Python, has too many bells and whistles, which can be a little bewildering to the novice. It is possible that they may be useful as one becomes more experienced with the language. Also I found that installing Python packages sometimes gives errors with Python versions 2.7 or 3.6. This will leave you scrambling to google to find how to fix these problems. These can be quite frustrating. R on the other hand makes installing R packages a breeze.
Anyway, this is my current opinion, and like all opinions, may change in the course of time. Let’s see!
I may write a follow up post with more advanced features of R and Python. So do keep checking! Long live R! Viva la Python!
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